Automatic and accurate detection of race conditions have been problematic. Many potential race conditions may be identified, however, many of the identified potential race conditions may not occur or may not, in fact, result in an adverse outcome. Typically, race conditions are reported that include a large number of either false positives, conditions that do not actually occur, or conditions that do not cause problems in the system. Such over-reporting of race conditions results in confusion and unnecessary increase in data analysis.
Thus, a system or method is needed in which race conditions in a system may be identified accurately such that race conditions that do not cause an adverse output may be filtered from the group of identified race conditions. In addition, a system or method is needed in which harmful race conditions may be efficiently identified.